Missingness Aware Gaussian Mixture Models
Calinski-Harabaz Index
Cluster Number Selection
Cluster Quality
Combine Multiple Imputations
Davies-Bouldin Index
Estimate Multivariate Normal Mixture
Fit Multivariate Mixture Distribution
Fit Multivariate Normal Distribution
Generate Imputation
Log likelihood for Fitted GMM
Log likelihood for Fitted MVN Model
Mean for Fitted GMM
Mean for Fitted MVN Model
MGMM: Missingness Aware Gaussian Mixture Models
Mixture Model Class
Show for Fitted Mixture Models
Mean Update for Mixture of MVNs with Missingness.
Multivariate Normal Model Class
Show for Multivariate Normal Models
Partition Data by Missingness Pattern
Print for Fitted GMM
Print for Fitted MVN Model
Reconstitute Data
Generate Data from Gaussian Mixture Models
Covariance for Fitted GMM
Covariance for Fitted MVN Model
Parameter estimation and classification for Gaussian Mixture Models (GMMs) in the presence of missing data. This package complements existing implementations by allowing for both missing elements in the input vectors and full (as opposed to strictly diagonal) covariance matrices. Estimation is performed using an expectation conditional maximization algorithm that accounts for missingness of both the cluster assignments and the vector components. The output includes the marginal cluster membership probabilities; the mean and covariance of each cluster; the posterior probabilities of cluster membership; and a completed version of the input data, with missing values imputed to their posterior expectations. For additional details, please see McCaw ZR, Julienne H, Aschard H. "Fitting Gaussian mixture models on incomplete data." <doi:10.1186/s12859-022-04740-9>.